Analyzing of salient features and classification of wine type based on quality through various neural network and support vector machine classifiers

Wine quality certification is crucial to the wine industry. Indian wine’s superior quality is well-known around the world. Wine quality certification is crucial to the wine industry. Our main objective in this study is to find out a machine-learning model based on experimental data that has been gathered from various places across India and available synthetic data in order to predict wine quality. We utilized 178 wine samples with 13 different physiochemical characteristics. All the features have been analyzed and shown the values of max, min, mean, Kurt, skewness, and standard deviation of each variable. Important attributes that can be selected by comparing the values of all the above-mentioned feature selection approaches were required very much to improve wine quality. Five neural network methods and six support vector methods were trained and tested on all features of the dataset. Narrow neural network, Wide neural network, Quadratic support vector machine and Medium Gaussian support vector machine — all these classifiers showed 97.8% accuracy when trained and evaluated with all features but Quadratic support vector machine achieved this accuracy with the lowest training time 0.92556 sec ad the highest prediction speed 6400 obs/sec. These results demonstrate that the anticipated and experimental responses are extremely well aligned, according to this accuracy and the preferred model is appropriate to classify wine quality based on physiochemical components. [Display omitted]